• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

粗粒化熵、力和结构。

Coarse-graining entropy, forces, and structures.

机构信息

Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.

出版信息

J Chem Phys. 2011 Dec 7;135(21):214101. doi: 10.1063/1.3663709.

DOI:10.1063/1.3663709
PMID:22149773
Abstract

Coarse-grained (CG) models enable highly efficient simulations of complex processes that cannot be effectively studied with more detailed models. CG models are often parameterized using either force- or structure-motivated approaches. The present work investigates parallels between these seemingly divergent approaches by examining the relative entropy and multiscale coarse-graining (MS-CG) methods. We demonstrate that both approaches can be expressed in terms of an information function that discriminates between the ensembles generated by atomistic and CG models. While it is well known that the relative entropy approach minimizes the average of this information function, the present work demonstrates that the MS-CG method minimizes the average of its gradient squared. We generalize previous results by establishing conditions for the uniqueness of structure-based potentials and identify similarities with corresponding conditions for the uniqueness of MS-CG potentials. We analyze the mapping entropy and extend the MS-CG and generalized-Yvon-Born-Green formalisms for more complex potentials. Finally, we present numerical calculations that highlight similarities and differences between structure- and force-based approaches. We demonstrate that both methods obtain identical results, not only for a complete basis set, but also for an incomplete harmonic basis set in Cartesian coordinates. However, the two methods differ when the incomplete basis set includes higher order polynomials of Cartesian coordinates or is expressed as functions of curvilinear coordinates.

摘要

粗粒化(CG)模型能够高效地模拟复杂过程,而这些过程无法通过更详细的模型进行有效研究。CG 模型通常使用力或结构驱动的方法进行参数化。本工作通过研究相对熵和多尺度粗粒化(MS-CG)方法,探讨了这些看似不同的方法之间的相似之处。我们证明,这两种方法都可以用一个信息函数来表示,该函数可以区分原子模型和 CG 模型生成的集合。虽然众所周知,相对熵方法最小化了这个信息函数的平均值,但本工作证明了 MS-CG 方法最小化了它的梯度平方的平均值。我们通过建立结构基势的唯一性条件,推广了以前的结果,并确定了与 MS-CG 势的唯一性相应条件的相似性。我们分析了映射熵,并扩展了 MS-CG 和广义 Yvon-Born-Green 形式用于更复杂的势。最后,我们提出了数值计算,突出了基于结构和力的方法之间的相似性和差异。我们证明了这两种方法不仅在完整基组中,而且在笛卡尔坐标系中的不完全谐波基组中都得到了相同的结果。然而,当不完全基组包括笛卡尔坐标的更高阶多项式或表示为曲线坐标的函数时,两种方法会有所不同。

相似文献

1
Coarse-graining entropy, forces, and structures.粗粒化熵、力和结构。
J Chem Phys. 2011 Dec 7;135(21):214101. doi: 10.1063/1.3663709.
2
The multiscale coarse-graining method. VII. Free energy decomposition of coarse-grained effective potentials.多尺度粗粒化方法。VII. 粗粒有效势的自由能分解。
J Chem Phys. 2011 Jun 14;134(22):224107. doi: 10.1063/1.3599049.
3
The multiscale coarse-graining method: assessing its accuracy and introducing density dependent coarse-grain potentials.多尺度粗粒化方法:评估其准确性并引入密度相关的粗粒化势。
J Chem Phys. 2010 Aug 14;133(6):064109. doi: 10.1063/1.3464776.
4
The multiscale coarse-graining method. X. Improved algorithms for constructing coarse-grained potentials for molecular systems.多尺度粗粒化方法。X. 用于构建分子系统粗粒化势能的改进算法。
J Chem Phys. 2012 May 21;136(19):194115. doi: 10.1063/1.4705420.
5
The multiscale coarse-graining method. IX. A general method for construction of three body coarse-grained force fields.多尺度粗粒化方法。IX. 构建三体粗粒化力场的一般方法。
J Chem Phys. 2012 May 21;136(19):194114. doi: 10.1063/1.4705417.
6
The role of many-body correlations in determining potentials for coarse-grained models of equilibrium structure.多体相关性在确定平衡结构粗粒模型势中的作用。
J Phys Chem B. 2012 Jul 26;116(29):8621-35. doi: 10.1021/jp3002004. Epub 2012 Jun 1.
7
Effective force coarse-graining.有效力粗粒化
Phys Chem Chem Phys. 2009 Mar 28;11(12):2002-15. doi: 10.1039/b819182d. Epub 2009 Feb 12.
8
The multiscale coarse-graining method. VIII. Multiresolution hierarchical basis functions and basis function selection in the construction of coarse-grained force fields.多尺度粗粒化方法。VIII. 粗粒化力场构建中的多分辨层次基函数和基函数选择。
J Chem Phys. 2012 May 21;136(19):194113. doi: 10.1063/1.4705384.
9
The multiscale coarse-graining method. V. Isothermal-isobaric ensemble.多尺度粗粒化方法。V. 等温等压系综。
J Chem Phys. 2010 Apr 28;132(16):164106. doi: 10.1063/1.3394862.
10
Multiscale coarse graining of liquid-state systems.液态系统的多尺度粗粒化
J Chem Phys. 2005 Oct 1;123(13):134105. doi: 10.1063/1.2038787.

引用本文的文献

1
Understanding the coarse-grained free energy landscape of phospholipids and their phase separation.理解磷脂的粗粒度自由能景观及其相分离。
Biophys J. 2025 Feb 18;124(4):620-636. doi: 10.1016/j.bpj.2024.12.030. Epub 2024 Dec 31.
2
Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models through Virtual Particles.利用机器学习通过虚拟粒子极大地扩展自下而上粗粒度模型的范围和准确性。
J Chem Theory Comput. 2023 Jul 25;19(14):4402-4413. doi: 10.1021/acs.jctc.2c01183. Epub 2023 Feb 20.
3
Bottom-up Coarse-Graining: Principles and Perspectives.
自底向上粗粒化:原理与展望。
J Chem Theory Comput. 2022 Oct 11;18(10):5759-5791. doi: 10.1021/acs.jctc.2c00643. Epub 2022 Sep 7.
4
A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules.探索映射空间之旅:表征大分子简化表示的统计和度量性质
Eur Phys J B. 2021;94(10):204. doi: 10.1140/epjb/s10051-021-00205-9. Epub 2021 Oct 12.
5
From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules.从系统建模到系统分析:分辨率水平和分辨率分布在生物分子计算机辅助研究中的影响
Front Mol Biosci. 2021 Jun 7;8:676976. doi: 10.3389/fmolb.2021.676976. eCollection 2021.
6
"Dividing and Conquering" and "Caching" in Molecular Modeling.分子建模中的“分而治之”和“缓存”。
Int J Mol Sci. 2021 May 10;22(9):5053. doi: 10.3390/ijms22095053.
7
A Deep Graph Network-Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins.一种深度图网络增强的采样方法,用于高效探索蛋白质简化表示空间。
Front Mol Biosci. 2021 Apr 29;8:637396. doi: 10.3389/fmolb.2021.637396. eCollection 2021.
8
Bottom-Up Coarse-Grained Modeling of DNA.DNA的自下而上粗粒度建模
Front Mol Biosci. 2021 Mar 17;8:645527. doi: 10.3389/fmolb.2021.645527. eCollection 2021.
9
Coarse graining molecular dynamics with graph neural networks.基于图神经网络的粗粒化分子动力学。
J Chem Phys. 2020 Nov 21;153(19):194101. doi: 10.1063/5.0026133.
10
An Information-Theory-Based Approach for Optimal Model Reduction of Biomolecules.基于信息论的生物分子最优模型约简方法。
J Chem Theory Comput. 2020 Nov 10;16(11):6795-6813. doi: 10.1021/acs.jctc.0c00676. Epub 2020 Oct 27.